






from collections.abc import Iterable
from typing import Generic, TypeVar

__a:     List[str]   					=					TypeVar('''_T''')
class 		SCREAMING_SNAKE_CASE__	(  Generic[_T]     ):

  '''simple docstring'''







  def __init__(      self		:			str       ,				lowerCamelCase		:			Iterable[_T] | None = None							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    list(iterable or []							)
         _UpperCAmelCase							=    []


  def __len__(      self		:			int							)						->	int:


         """simple docstring"""
         return len(self._stacka							) + len(self._stacka							)


  def __repr__(      self		:			Any							)						->	str:


         """simple docstring"""
         return f"""Queue({tuple(self._stacka[::-1] + self._stacka							)})"""


  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			_T							)						->	None:


         """simple docstring"""
         self._stacka.append(lowerCamelCase							)





  def  lowerCamelCase       (      self		:			str							)						->	_T:


         """simple docstring"""
         _UpperCAmelCase							=    self._stacka.pop
         _UpperCAmelCase							=    self._stacka.append

         if not self._stacka:
                while self._stacka:
                       stacka_append(stacka_pop()							)

         if not self._stacka:
                raise IndexError("""Queue is empty"""							)
         return self._stacka.pop()


if __name__ == "__main__":
  from doctest import testmod

  testmod()

"""simple docstring"""



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Dict			     =					StableDiffusionSAGPipeline
    a   :					Any			     =					TEXT_TO_IMAGE_PARAMS
    a   :					Optional[Any]			     =					TEXT_TO_IMAGE_BATCH_PARAMS
    a   :					Optional[int]			     =					TEXT_TO_IMAGE_IMAGE_PARAMS
    a   :					Optional[int]			     =					TEXT_TO_IMAGE_IMAGE_PARAMS
    a   :					Tuple			     =					False







    def   _snake_case       (      self      :							List[Any]  )		->Tuple:






        '''simple docstring'''






        torch.manual_seed(0  )
        _UpperCAmelCase        = UNetaDConditionModel(
            block_out_channels=(32, 64)  ,       layers_per_block=2  ,       sample_size=32  ,       in_channels=4  ,       out_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,       cross_attention_dim=32  ,       )
        _UpperCAmelCase        = DDIMScheduler(
            beta_start=0.0_0_0_8_5  ,       beta_end=0.0_1_2  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__UpperCamelCase  ,       set_alpha_to_one=__UpperCamelCase  ,       )
        torch.manual_seed(0  )
        _UpperCAmelCase        = AutoencoderKL(
            block_out_channels=[32, 64]  ,       in_channels=3  ,       out_channels=3  ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,       latent_channels=4  ,       )
        torch.manual_seed(0  )
        _UpperCAmelCase        = CLIPTextConfig(
            bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=32  ,       intermediate_size=37  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=10_00  ,       )
        _UpperCAmelCase        = CLIPTextModel(__UpperCamelCase  )
        _UpperCAmelCase        = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

        _UpperCAmelCase        = {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Any=0  )		->Any:






        '''simple docstring'''






        if str(__UpperCamelCase  ).startswith("""mps"""  ):
            _UpperCAmelCase        = torch.manual_seed(__UpperCamelCase  )
        else:
            _UpperCAmelCase        = torch.Generator(device=__UpperCamelCase  ).manual_seed(__UpperCamelCase  )
        _UpperCAmelCase        = {
            """prompt""": """.""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 1.0,
            """sag_scale""": 1.0,
            """output_type""": """numpy""",
        }
        return inputs







    def   _snake_case       (      self      :							Optional[Any]  )		->Optional[int]:






        '''simple docstring'''






        super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







@slow
@require_torch_gpu
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							Union[str, Any]  )		->str:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def   _snake_case       (      self      :							Tuple  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  )

        _UpperCAmelCase        = output.images

        _UpperCAmelCase        = image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase        = np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2







    def   _snake_case       (      self      :							Union[str, Any]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  )

        _UpperCAmelCase        = output.images

        _UpperCAmelCase        = image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase        = np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2







    def   _snake_case       (      self      :							str  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       width=7_68  ,       height=5_12  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  ,       )

        _UpperCAmelCase        = output.images

        assert image.shape == (1, 5_12, 7_68, 3)